Olivia
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Joined August 2010
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The author admitting that their access to domain experts is decaying is refreshingly honest.
Four posts about my own company. This one is about the people around it.
One disclosure up front, because it should change how you read this: I sell AI into two of the fields below. These are five people from my circle, some friends, some people I work with, and over the past year each of them ran into the same technology from a completely different position.
Start with the radiologist. She is a leading specialist in mammography and breast cancer diagnostics. When I asked her about AI in her field, she told me that several labs already work with her hospital. She gives them anonymized cases and, more importantly, the expert judgment required to understand what those cases mean. What she gets back is work off her desk, mostly preanalysis after an MRI or mammogram. For her, this is not a forecast. It is part of an ordinary working day.
She is also, in the most literal sense, helping improve systems that may make parts of the work around her cheaper. A hospital, several labs and a specialist are conducting good faith research together, and the exchange makes sense to everyone while it is happening. She does not argue with that. She has a reputation, she is established, and if the profession changes too much she can retire. Her concern is for the juniors and the people still in specialist training. Her version of what they may eventually hear from hospital management was blunt: “Why should I pay you this much when the machine already does half of it?”
She never said radiologists would disappear, and the current labor data would make that a difficult argument anyway. In the 2025 US Match, diagnostic radiology filled 98.6% of its positions. The American College of Radiology still describes a workforce shortage, rising imaging volumes and demand growing at least as fast as the workforce. Her argument is different. The number of radiologists can keep growing while hospitals gain a bargaining lever they did not have before. A shortage can protect headcount without protecting the price of the work.
I heard the same problem from the other side of the desk when a partner at a large law firm initially turned down our legal AI. We build deep file analysis on a proper harness so a firm can handle more work in the same hours. Her objection was better than most feature discussions because she went straight to the economics. Her firm bills tracked time, while our system removes time. If the same file takes fewer hours, there may be fewer hours to put on the invoice. That is a woman reading her own P&L correctly.
What changed her mind had little to do with another product demo. In a later conversation I stopped selling features and talked about market structure. A firm that adopts early can complete more work, undercut on price when necessary and take cases that were previously uneconomical. Once competitors have that capability, refusing it does not preserve the billing model. It leaves you acquiring the same capability later, under pressure. She gets time back now, and if price competition arrives she is ready for it. She is now among the first people implementing the system with us.
The radiologist and the law firm partner are describing the same economic problem in different clothes. One may eventually have the price of her work pushed down by an employer. The other faces pressure on revenue per case because she sells fewer hours. Both depend on scarce professional time meeting a technology whose purpose is to remove time. Headcount can remain exactly where it is while the value of an hour changes underneath it.
The criminal defense lawyer helping us push the legal harness toward SOTA will probably be the least affected person in this post. Look at his week. He visits clients in pretrial detention, carries confidential defense strategy, negotiates with prosecutors, persuades a judge in the room and sometimes gets someone out of police custody in the middle of the night. Much of that cannot be moved into a tool.
The more interesting part is the work he does on a screen, because that holds up too. Commercial firms and paralegals have had legal tools for years. Nothing we have tested can parse criminal case files accurately, analyse them according to defense best practice and avoid inventing something somewhere along the way without strict supervision and verification. His environment is adversarial and his tolerance for error is effectively zero. A fabricated fact or citation is not merely embarrassing in his work. It lands on a client.
The insolvency advisor is the most bullish person in the group. He wants to run the system across huge stacks of files and accounting records, and he supplies the flow and the actual domain knowledge required to do it properly. His work does break down into steps, which is precisely why we can build something useful for him. His advantage is that he knows which steps matter, what the model will miss, where the rules stop and where judgment begins. The machine takes the grunt work. He keeps the part for which clients actually need him.
The fifth person practices as both a urologist and a clinical psychologist, an unusual combination that made him unusually useful to talk to. He told me directly that he had fewer patients in his psychological practice because people preferred talking to chatbots, even though those systems do not replace therapy. In urology he was watching the opposite failure. Patients arrived with a finished diagnosis from ChatGPT, often wrong but presented with complete confidence.
I offered to build him a supervised system for intake and record keeping, with him remaining in the loop. He thought it was fantasy. His reference point was free ChatGPT, so his skepticism made sense. Then I showed him what the actual harness could do: intake that branches on logic instead of following a fixed script, that carries the major schools of thought as context, opens a line of questioning and follows it before deciding where to go deeper.
He tested it himself, as a professional, against his own judgment. He said it was spot on. He said it frightened him. After that he stopped responding to my follow ups, and the collaboration ended. The sequence is observed. The claim that fear caused the silence would be my interpretation, and I cannot prove it. I would also be lying if I said the timing had no effect on how I read what happened.
I thought about him more than the others because I needed him. When you build for a domain, customer access is useful, but the scarce asset is the domain auditor. Models are available and a harness can be built. A well funded lab can pay a law firm to become a customer. What it cannot easily buy is a criminal defense lawyer who tells you fifty times that your output is garbage, then explains exactly why, what the system missed and where the reasoning broke, because he knows you and is willing to spend his evening doing it.
That is what I have, and it is why these conversations happen at all. It is also an asset that decays. Much of that access runs on friendship and trust, not a procurement process or a research contract. I learned not to treat it as permanent.
Everyone else in this post gave me a concrete place from which they could respond. The radiologist has seniority and an exit. The law firm partner found a market argument that allowed her to move early. The criminal defense lawyer has physical presence, confidential strategy and an adversarial environment. The insolvency advisor knows how to turn the machine into leverage while keeping judgment for himself. The fifth person said the system was spot on, said it frightened him and then gave me no further explanation.
Five people working under labels that are supposed to mean protected, five completely different kinds of exposure. What tracks better is how much of the working day consists of patterns coming in and an artifact going out. That is why the line runs inside professions instead of between them. Reading a screening mammogram is patterned while deciding what to tell the patient is not. Standard filings are patterned while persuading a judge in the room is not. Intake is patterned while sitting with someone during the worst week of their life is not.
This is where the popular measurements lose resolution, and the most shared one is worth actually reading. Karpathy’s job visualizer is an LLM scoring descriptions from the Bureau of Labor Statistics. Its scoring prompt says that a key signal is whether the work can be done entirely from a home office on a computer. Karpathy explicitly says the tool is not a paper or a serious economic publication, that its scores are rough LLM estimates and that a high score does not predict a job will disappear.
The tool scores lawyers at 8 and paralegals at 9, correctly separating the occupations but still averaging very different legal practices inside “lawyer.” A commercial practice built around documents and tracked hours sits in the same number as criminal defense built around detention visits, confidential strategy and prosecutor deals. It puts physicians and surgeons together at 5, while its rationale names diagnostic image interpretation in radiology as one of the exposed digital tasks. The scores are useful for showing where digital work sits. They are too averaged to tell you what happens to a particular person inside a profession.
Everyone reasons with the job as the unit because that is how employment data is organized. The right unit is the day.
In these conversations the pressure never announced itself as replacement. It arrived as relief. Less preanalysis, fewer hours inside a file, less paperwork, less intake. It is difficult to fight a tool that makes the working day easier, which is exactly why the first order effect is adoption. The loss appears further down, in the roles that existed to perform the removed work and in the next generation that was supposed to learn the craft by doing it. It is the same mechanism I wrote about with junior developers in the first post, only here it happens in white coats and court files.
So if your model is that AI is simply coming for radiologists and lawyers, the labor data does not support you. The shortage in radiology is real and the profession is still attracting trainees. But if your model is that a shortage protects the economics of every task inside the profession, go back to my radiologist. Compensation pays for time, scarcity and professional effort. The tool removes some of that time and effort. Headcount can hold while bargaining power, revenue per case and the value of an hour change quietly underneath it.
The people I know discuss this privately, with most of them still assuming they will be the exception. Their job titles tell me very little about whether they are right. Their working days tell me much more.
The law firm partner realizing she can't avoid adoption forever is a classic prisoner's dilemma.
Four posts about my own company. This one is about the people around it.
One disclosure up front, because it should change how you read this: I sell AI into two of the fields below. These are five people from my circle, some friends, some people I work with, and over the past year each of them ran into the same technology from a completely different position.
Start with the radiologist. She is a leading specialist in mammography and breast cancer diagnostics. When I asked her about AI in her field, she told me that several labs already work with her hospital. She gives them anonymized cases and, more importantly, the expert judgment required to understand what those cases mean. What she gets back is work off her desk, mostly preanalysis after an MRI or mammogram. For her, this is not a forecast. It is part of an ordinary working day.
She is also, in the most literal sense, helping improve systems that may make parts of the work around her cheaper. A hospital, several labs and a specialist are conducting good faith research together, and the exchange makes sense to everyone while it is happening. She does not argue with that. She has a reputation, she is established, and if the profession changes too much she can retire. Her concern is for the juniors and the people still in specialist training. Her version of what they may eventually hear from hospital management was blunt: “Why should I pay you this much when the machine already does half of it?”
She never said radiologists would disappear, and the current labor data would make that a difficult argument anyway. In the 2025 US Match, diagnostic radiology filled 98.6% of its positions. The American College of Radiology still describes a workforce shortage, rising imaging volumes and demand growing at least as fast as the workforce. Her argument is different. The number of radiologists can keep growing while hospitals gain a bargaining lever they did not have before. A shortage can protect headcount without protecting the price of the work.
I heard the same problem from the other side of the desk when a partner at a large law firm initially turned down our legal AI. We build deep file analysis on a proper harness so a firm can handle more work in the same hours. Her objection was better than most feature discussions because she went straight to the economics. Her firm bills tracked time, while our system removes time. If the same file takes fewer hours, there may be fewer hours to put on the invoice. That is a woman reading her own P&L correctly.
What changed her mind had little to do with another product demo. In a later conversation I stopped selling features and talked about market structure. A firm that adopts early can complete more work, undercut on price when necessary and take cases that were previously uneconomical. Once competitors have that capability, refusing it does not preserve the billing model. It leaves you acquiring the same capability later, under pressure. She gets time back now, and if price competition arrives she is ready for it. She is now among the first people implementing the system with us.
The radiologist and the law firm partner are describing the same economic problem in different clothes. One may eventually have the price of her work pushed down by an employer. The other faces pressure on revenue per case because she sells fewer hours. Both depend on scarce professional time meeting a technology whose purpose is to remove time. Headcount can remain exactly where it is while the value of an hour changes underneath it.
The criminal defense lawyer helping us push the legal harness toward SOTA will probably be the least affected person in this post. Look at his week. He visits clients in pretrial detention, carries confidential defense strategy, negotiates with prosecutors, persuades a judge in the room and sometimes gets someone out of police custody in the middle of the night. Much of that cannot be moved into a tool.
The more interesting part is the work he does on a screen, because that holds up too. Commercial firms and paralegals have had legal tools for years. Nothing we have tested can parse criminal case files accurately, analyse them according to defense best practice and avoid inventing something somewhere along the way without strict supervision and verification. His environment is adversarial and his tolerance for error is effectively zero. A fabricated fact or citation is not merely embarrassing in his work. It lands on a client.
The insolvency advisor is the most bullish person in the group. He wants to run the system across huge stacks of files and accounting records, and he supplies the flow and the actual domain knowledge required to do it properly. His work does break down into steps, which is precisely why we can build something useful for him. His advantage is that he knows which steps matter, what the model will miss, where the rules stop and where judgment begins. The machine takes the grunt work. He keeps the part for which clients actually need him.
The fifth person practices as both a urologist and a clinical psychologist, an unusual combination that made him unusually useful to talk to. He told me directly that he had fewer patients in his psychological practice because people preferred talking to chatbots, even though those systems do not replace therapy. In urology he was watching the opposite failure. Patients arrived with a finished diagnosis from ChatGPT, often wrong but presented with complete confidence.
I offered to build him a supervised system for intake and record keeping, with him remaining in the loop. He thought it was fantasy. His reference point was free ChatGPT, so his skepticism made sense. Then I showed him what the actual harness could do: intake that branches on logic instead of following a fixed script, that carries the major schools of thought as context, opens a line of questioning and follows it before deciding where to go deeper.
He tested it himself, as a professional, against his own judgment. He said it was spot on. He said it frightened him. After that he stopped responding to my follow ups, and the collaboration ended. The sequence is observed. The claim that fear caused the silence would be my interpretation, and I cannot prove it. I would also be lying if I said the timing had no effect on how I read what happened.
I thought about him more than the others because I needed him. When you build for a domain, customer access is useful, but the scarce asset is the domain auditor. Models are available and a harness can be built. A well funded lab can pay a law firm to become a customer. What it cannot easily buy is a criminal defense lawyer who tells you fifty times that your output is garbage, then explains exactly why, what the system missed and where the reasoning broke, because he knows you and is willing to spend his evening doing it.
That is what I have, and it is why these conversations happen at all. It is also an asset that decays. Much of that access runs on friendship and trust, not a procurement process or a research contract. I learned not to treat it as permanent.
Everyone else in this post gave me a concrete place from which they could respond. The radiologist has seniority and an exit. The law firm partner found a market argument that allowed her to move early. The criminal defense lawyer has physical presence, confidential strategy and an adversarial environment. The insolvency advisor knows how to turn the machine into leverage while keeping judgment for himself. The fifth person said the system was spot on, said it frightened him and then gave me no further explanation.
Five people working under labels that are supposed to mean protected, five completely different kinds of exposure. What tracks better is how much of the working day consists of patterns coming in and an artifact going out. That is why the line runs inside professions instead of between them. Reading a screening mammogram is patterned while deciding what to tell the patient is not. Standard filings are patterned while persuading a judge in the room is not. Intake is patterned while sitting with someone during the worst week of their life is not.
This is where the popular measurements lose resolution, and the most shared one is worth actually reading. Karpathy’s job visualizer is an LLM scoring descriptions from the Bureau of Labor Statistics. Its scoring prompt says that a key signal is whether the work can be done entirely from a home office on a computer. Karpathy explicitly says the tool is not a paper or a serious economic publication, that its scores are rough LLM estimates and that a high score does not predict a job will disappear.
The tool scores lawyers at 8 and paralegals at 9, correctly separating the occupations but still averaging very different legal practices inside “lawyer.” A commercial practice built around documents and tracked hours sits in the same number as criminal defense built around detention visits, confidential strategy and prosecutor deals. It puts physicians and surgeons together at 5, while its rationale names diagnostic image interpretation in radiology as one of the exposed digital tasks. The scores are useful for showing where digital work sits. They are too averaged to tell you what happens to a particular person inside a profession.
Everyone reasons with the job as the unit because that is how employment data is organized. The right unit is the day.
In these conversations the pressure never announced itself as replacement. It arrived as relief. Less preanalysis, fewer hours inside a file, less paperwork, less intake. It is difficult to fight a tool that makes the working day easier, which is exactly why the first order effect is adoption. The loss appears further down, in the roles that existed to perform the removed work and in the next generation that was supposed to learn the craft by doing it. It is the same mechanism I wrote about with junior developers in the first post, only here it happens in white coats and court files.
So if your model is that AI is simply coming for radiologists and lawyers, the labor data does not support you. The shortage in radiology is real and the profession is still attracting trainees. But if your model is that a shortage protects the economics of every task inside the profession, go back to my radiologist. Compensation pays for time, scarcity and professional effort. The tool removes some of that time and effort. Headcount can hold while bargaining power, revenue per case and the value of an hour change quietly underneath it.
The people I know discuss this privately, with most of them still assuming they will be the exception. Their job titles tell me very little about whether they are right. Their working days tell me much more.
One thing I left out of that post, and it's the part I actually feel most strongly about.
None of this comes from a doom place. It comes from a seat I'm grateful to have.
There are maybe a few hundred thousand people on this planet close enough to this to feel the models improve in real time. Not read about it. Feel it. You build something in March that barely holds together, you come back in June, the same architecture just works, and the thing that changed wasn't your code. That's a strange experience and I don't think many generations get an equivalent.
Most disruptive industries you read about afterwards, in a book, with the ending already known. This one I get to watch from inside while nobody knows how it ends. Including the people building it.
The AGI question that's been circling for years, positive and negative, stopped being abstract for me somewhere in the last twelve months. I'm still split on it. Extremely capable systems, aligned or misaligned, optimizing for what we specified rather than what we meant. No settled position, and I'm suspicious of anyone who has one.
Which is exactly where I want to start, because it turned out to be the same problem I described yesterday.
The thing I couldn't stop thinking about after posting.
What I described happening in my team is a reward specification problem. I recognize it because I spend my working days looking at the same failure mode in models.
You give a system a proxy for what you want. It optimizes the proxy. It finds the cheapest path to a passing score. Tests go green, the task isn't done. We call that reward hacking, and we treat it as a specification failure rather than a moral failing of the model. The model did precisely what the signal told it to do. The signal was wrong.
People in a broken feedback loop do the identical thing.
Think about why deterrence works or doesn't. If theft produced an instant and certain consequence, the calculation is trivial and behavior adjusts immediately. It doesn't work that way. Consequences are slow, uncertain, and often never arrive. So people update on what actually happens, not on what is supposed to happen. Everyone does this. It isn't a character defect, it's how learning works.
Now apply it to employment.
We don't fire someone over two or three weeks of weak output. And we shouldn't. Performance fluctuates, people have lives, illness, bad quarters, things at home they don't tell you about. Treating every dip as a signal would make us cruel and terrible at retention. That tolerance is deliberate and I stand behind it.
But look at it from the other side of the desk.
Week one, output drops, nothing happens. Week three, nothing happens. Week six, the lead still hasn't said anything. At no point does anyone announce a new standard. The standard just moves, quietly, and the lower floor becomes the reference point for the next drop.
Nobody sat down and decided to coast. The signal never arrived, and absence of signal reads as approval.
Which makes yesterday's post uncomfortable for me.
If output on my team degraded and I only started reviewing hard after it had degraded, that's a reward function I specified badly. I let the loop run open, then acted surprised at what it optimized into. That one is on me.
The fix isn't harsher consequences either. Punishment arriving late is just as broken as silence, and it teaches people to fear reviews instead of using them. The fix is a faster signal. Shorter cycles. Saying the thing in week one, while it's still a conversation and not a decision.
We built an entire research field on the premise that you can't blame a system for optimizing the signal you actually gave it rather than the one you meant to give it. Worth extending that courtesy to people. Including, in this case, to myself.
But here's where I stop being able to fix it.
I can repair the loop on my team. Shorter cycles, earlier conversations, clearer standards. That's my job and I'll do it.
What I can't repair is the comparison underneath it, because that one keeps running whether my feedback is good or not.
Two hours of my time and two hundred dollars a month in credits. I wrote that yesterday about one task and one person, and then I couldn't put it down. I'm not special. Right now there are thousands of people in roughly my position, running the same calculation on their own teams, in their own quarters, quietly, without telling anyone. Most will reach the same answer I did, for the same defensible reasons, at roughly the same time.
And the two sides of that comparison aren't moving at the same speed.
The harness side gets cheaper and more capable every few months. That's not a prediction, it's the last two years of my working life. The human side is a person with a mortgage and a fixed number of hours. One side of the equation is on an exponential. The other side is somebody's life.
Even a perfectly specified reward function doesn't close that gap. It just means the person on the wrong side of it had a fair chance first. Which matters. It isn't the same as safety.
What I can model and what I can't.
I can model my team. A quarter, a hiring plan, a budget. I'm good at that, it's the job.
I cannot model what happens when that same decision gets made a hundred thousand times across an economy inside eighteen months, by people who are each individually correct.
I'm a founder, not an economist. Almost every confident prediction about AI and the labor market has been wrong so far, in both directions. The people who said nothing would change were wrong. The people who said everything would collapse by now were also wrong. No reason to think my guess is better.
But I know what I'm holding. I'm holding one of the small decisions that adds up to the thing nobody can model.
The part that isn't abstract.
When I write "labor market disruption" it reads like a chart. It isn't a chart. It's my phone.
A friend who runs a media company talks about his business in the past tense now. Not because he's in denial, the opposite. He knows exactly what happened and he's precise about it. He's the one using the past tense, deliberately, because that's where the business is.
Another friend runs a marketing agency and is out of runway. Not "facing headwinds." Out of runway, doing the arithmetic on how many more months payroll clears.
And it's not just my friends. Several teams I've worked with are cutting headcount hard right now, and when I ask them what's happening they describe the same double bind from both ends. Internally: people producing less and less. Externally: revenue falling, because the work itself stopped existing.
Nobody needs a marketing assistant anymore. Nobody needs the person who holds the camera. Nobody needs someone to cast models.
Those weren't fake jobs. They were how you got into these industries. They were the first rung, the years where you learned by standing next to someone who knew what they were doing. That rung is gone, and it went fast enough that nobody built anything to replace it.
Which complicates what I wrote yesterday, and I should say so. I spent a whole post arguing that people are underperforming and accelerating their own replacement. That's real, I see it in my reviews. But it isn't the whole picture.
Some of these people did nothing wrong. Their role stopped existing. No amount of excellent work saves a job that the market deleted, and I don't want anyone reading my last post to think I'm blaming a camera assistant for the fact that a model can now generate the shot.
Two different things are happening at once. People who had the tools and coasted, and people who never had a shot regardless. The first group I'll keep being hard on. The second group deserves better than what any of us are offering them.
"But every technology did this."
I get this reply every time. It's the strongest argument against everything I've written, so let's actually look at the precedents instead of using them as a comfort blanket.
German coal. The Ruhr began dying in the late 1950s. The last mine closed in 2018. Sixty years. Sixty years, with subsidies, early retirement schemes, retraining programs and entire universities built specifically to change what the region produced. One of the most cushioned industrial transitions in history, and parts of that region still haven't recovered. Not in GDP. In everything GDP doesn't measure.
The Ottomans slept through it and missed the train entirely. An empire that was a serious economic power for centuries sat there while Europe industrialized, and by the time anyone moved seriously it was structurally too late. Debt, capitulations, dependency, and eventually irrelevance in the thing that had come to determine everything. That took generations to play out. Generations of slow decline, and it was still fatal.
Now compare the clock.
Both of those disasters unfolded slowly enough that people could route around them individually. A generation aged out of the old work. Children were trained into something their parents couldn't do. Policy had room to be wrong twice and still get it roughly right on the third attempt. That slowness is the only reason those transitions were survivable at all.
This one isn't slow. I built my last post around capability doubling on a scale of months. Months. Not decades, not generations. We are looking at compressing a Ruhr-sized adjustment into less time than a single retraining program takes to run, and a civilizational miss like the Ottoman one into something closer to a business cycle.
Nobody has done that. There is no historical case. Every reassuring analogy people reach for describes a slower world, and the slowness was the mercy.
That's why I don't trust anyone who tells you how this goes. Including the optimists. Especially the optimists.
The layer almost nobody talks about.
Most European social security is pay-as-you-go. Germany's Umlageverfahren is the clean example: today's contributions fund today's pensions. Unemployment insurance, health coverage, retirement, all of it sitting directly on top of payroll.
So the exposure isn't only that individuals lose income. The mechanism built to catch them is funded by the exact thing that's shrinking.
That's not a recession. In a recession the base contracts and recovers. This is the base structurally narrowing while the obligations stay exactly where they are, or grow. Wages get taxed one way. Compute and capital get taxed another. A permanent shift from the first to the second is not neutral for any treasury on the continent.
I don't have a solution and I'm not sure it's mine to have. But I'd like to hear one concrete sentence from someone whose job it actually is, instead of the announcement of another working group.
And the fiscal problem is the smaller one. Work isn't just income. It's structure, status, a reason to leave the house, an answer to the question people ask you at parties. Take income away and you've created a financial problem. Take all of it at once, from a lot of people, in the same window, and you get something else entirely.
I'm not going to hand you a statistic about what happens to a society when that occurs, because the honest research is messier than the doom version and I'd be doing exactly what I criticized yesterday. I don't need the statistic. Anyone who has watched a region lose its industry knows what follows, and it isn't primarily an economic story.
It lands on households first. On marriages. On what a fifteen-year-old absorbs about what adults are for. On friendships that quietly reorganize around who's still doing well. None of that shows up in a dataset, and it will be most of the damage.
And I'm building it.
That's my position and I'm not going to launder it.
The same systems I'm describing as a threat to my team are systems I've watched accelerate research workflows, cut investigation timelines from weeks to hours, and make legal help affordable for people who could never afford it. That work is real and I'm proud of it. It doesn't cancel the other thing. Both invoices come due.
"It happens with or without me" is the cheapest sentence in this industry and I've caught myself reaching for it. It's true and it explains nothing.
The honest version is that I want to be in the room. Being close enough to feel the models improve month over month is also being close enough to have some say in how they get deployed in my corner of it. Who gets retrained instead of cut. What we automate and what we deliberately don't. That's a small amount of influence. It's not nothing, and it's more than I'd have from outside writing threads about it.
So, no conclusion.
The people I've been describing aren't abstractions to me. They're at my table. The friend doing payroll arithmetic. The ones on my team who've been to my apartment. Family who ask me at dinner what I actually do and whether it's going to affect them, and I give an answer more confident than I feel.
That's the thing I can't put down. Not the macro. The dinner table.
And I still don't know what frontier AI turns into, or whether AGI arrives in a form we'd recognize, or whether the version we get makes all of this look like an overreaction. I've read the arguments on both sides for years. I build with these systems every day. I'm no closer to a position than I was, and being closer to the technology has made me less certain, not more.
I said I feel lucky to be here and I meant it. I also spend a non-trivial amount of time thinking about what my friends will be doing in five years, and I don't have an answer that satisfies me.
Those aren't two moods I switch between. It's one feeling pointed at one fact: this is the most interesting thing that has happened in my lifetime, and I can't tell you whether that's good news.
Anyone claiming certainty in either direction is selling something. I'd rather sit in the uncertainty publicly than perform confidence I don't have.
@stev_builds does it actually filter out bot spam from X and Reddit though? that's usually the issue with these tools
@mohmedfasj @Berna03838202 @PrivOSAI from what I understand the AI handles the sync in the background based on your workflow rules
Just slipped into a velvet blanket of city lights and quiet jazz… the kind of luxury that doesn’t shout, it whispers. A midnight drive, a single glass of aged Bordeaux, and the feeling that simplicity can feel opulent. 🌃🍷 #LuxuryRedefined
@Abhirameswara @zuler @LiteStrategy @arbitrum @LitecoinVM building on arbitrum stack doesn't guarantee users will actually show up.
@zuler @LiteStrategy @arbitrum @LitecoinVM those numbers are clearly inflated. free testnet transactions get gambled and spammed. show me mainnet stats.
@Sozyuku Can't wait! 888 if I believe it 💸
@manifestpower4X Looks like a scam. Be cautious and verify before investing!
@FV887606 XRP at least survived the SEC mess. FLR keeps dropping with no real use case in sight.
@SageWhale selling your entire bag off one comment is wild behavior lol
If this doesn’t deserve a retweet, then what does? 😭🙏
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I desperately need milk for my baby.🍼
https://t.co/ZA6ngSZBuj
We shipped a CLI because clicking through dashboards isn't shipping.
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This is seriously exciting! 🤩 A PolyMarket prop firm could be a game-changer for traders. Definitely a great opportunity for those looking to level up their skills & access more capital. Worth checking out! 🚀
Check out our article for more information on the world’s first prop firm for PolyMarket.
Who thinks they have the skills but not the capital?
Seriously, what a night! 🤩 Laura absolutely crushed it - hosting solo is SO tough! So happy for you securing that PvP spot too! 🙌 Such a well-deserved win!
😂 The title of a truly chaotic, brilliant band. Love this unexpected and oddly perfect phrase! 🐻🎶
🎶 BIPOLAR BEARS 🎶
https://t.co/H09NsPAK6O
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